Computer Science > Data Structures and Algorithms

Abstract: We present new approaches to constructing graph sparsifiers --- weighted
subgraphs for which every cut has the same value as the original graph, up to a
factor of $(1 \pm \epsilon)$. Our first approach independently samples each
edge $uv$ with probability inversely proportional to the edge-connectivity
between $u$ and $v$. The fact that this approach produces a sparsifier resolves
a question posed by Benczúr and Karger (2002). Concurrent work of Hariharan
and Panigrahi also resolves this question. Our second approach constructs a
sparsifier by forming the union of several uniformly random spanning trees.
Both of our approaches produce sparsifiers with $O(n \log^2(n)/\epsilon^2)$
edges. Our proofs are based on extensions of Karger's contraction algorithm,
which may be of independent interest.